@inproceedings{mobin-islam-2025-luxveri-genai,
    title = "{L}ux{V}eri at {G}en{AI} Detection Task 3: Cross-Domain Detection of {AI}-Generated Text Using Inverse Perplexity-Weighted Ensemble of Fine-Tuned Transformer Models",
    author = "Mobin, MD. Kamrujjaman  and
      Islam, Md Saiful",
    editor = "Alam, Firoj  and
      Nakov, Preslav  and
      Habash, Nizar  and
      Gurevych, Iryna  and
      Chowdhury, Shammur  and
      Shelmanov, Artem  and
      Wang, Yuxia  and
      Artemova, Ekaterina  and
      Kutlu, Mucahid  and
      Mikros, George",
    booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.genaidetect-1.41/",
    pages = "352--357",
    abstract = "This paper presents our approach for Task 3 of the GenAI content detection workshop at COLING-2025, focusing on Cross-Domain Machine-Generated Text (MGT) Detection. We propose an ensemble of fine-tuned transformer models, enhanced by inverse perplexity weighting, to improve classification accuracy across diverse text domains. For Subtask A (Non-Adversarial MGT Detection), we combined a fine-tuned RoBERTa-base model with an OpenAI detector-integrated RoBERTa-base model, achieving an aggregate TPR score of 0.826, ranking 10th out of 23 detectors. In Subtask B (Adversarial MGT Detection), our fine-tuned RoBERTa-base model achieved a TPR score of 0.801, securing 8th out of 22 detectors. Our results demonstrate the effectiveness of inverse perplexity-based weighting for enhancing generalization and performance in both non-adversarial and adversarial MGT detection, highlighting the potential for transformer models in cross-domain AI-generated content detection."
}